Copy_of_Uplimit_Week1_LectureNotebook.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nicole1020/ATM_with_AI/blob/master/Copy_of_Uplimit_Week1_LectureNotebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This notebook is part of the course: [Building AI products with OpenAI](https://corise.com/go/building-ai-products-with-openai-MWKY3) and is created by Sidharth Ramachandran as part of the Lecture session for this course."
      ],
      "metadata": {
        "id": "TodxxTKKfU9h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Working with the OpenAI API\n",
        "\n",
        "Welcome to the lecture notebook that is part of the course - **Building AI products with OpenAI**. In this notebook, we show you some basic aspects of working with the OpenAI ecosystem using a web interface (Playground) and directly with the API. This is meant as a way to familiarize yourself with the OpenAI ecosystem so that you can make faster progress in the project section. Please also refer to the content on the CoRise platform that provides more detailed information and background into the various aspects of the OpenAI ecosystem."
      ],
      "metadata": {
        "id": "885gcSvYfXjO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Demo of OpenAI Playground\n",
        "\n",
        "In order to use OpenAI APIs, please create an account with OpenAI by visiting this [link](https://platform.openai.com/) and creating an account with a login of your choice. This will allow you to access the API - different from the ChatGPT front-end application. Note that when you create a completely new account, you will recieve $5 of credit which should be enough for the purposes of this project.\n",
        "\n",
        "Once you have created the account, please navigate to the Playground page as shown in the screenshot below -"
      ],
      "metadata": {
        "id": "kvIXktZwhUYI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Screenshot 2023-07-25 at 21.28.04.png](data:image/png;base64,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bHen0FdJBr8mTpxmKyKdeebA0OCXq8CiAI0qstBKXmDWrMhwSzJXMG/WrHn5Cn75e9a7dw+vek4j/yRz+PBh75qxzaxevd7s33/AVvbSo79il64zI0ZcErPeihVrvT5E+jRs2NmmUaP60fl169aJPk+VJ/PnL7WhL/Xn9ttHUKEyVd6YNO2HgpeNG8eeS0GHot9GNARSTeDKHoNMFa/ao7/tPrTfrNu1zSzass5s27fb/PbDF819515jq3/5l+M5AggggAACCCCAAAIIIIAAAggggAACCCCAAALpKlCqwS8XvBKWQldhlbsKgpks8OW2o31lZo5NGALSBcD8ITG3Tnl+fPXV923oSze8dVN76NAzY4Iz7dq18byMGTSor3nuuVfNt99utzfMFRBzoYjy7OOO7YYbrjBnnZXpBeKaxfi4+TyWnICGj9IwbUFt7dqN5uWX3zEaplRDcSqwePbZg4IWZRoCBRY47bR+pkOHtqZOnTreMIGpF/Ip8AGl+AqquPXFF1/bXl500bnm7bc/NHPnLjRXXXWBF7hNHJox7HAGDuxtunfvHDh79+693nfYO+brrxebd9752AZ5Ff5T0/dgfOVAr0vR4JdCMK1btwjcbqpMVMUvNQWUqVCXKu9K+vZj0KB+oedS+h4VPa8oAtf3PtvUqBr8jzVWbt9sHprxplmz41tz38fjzUPDbjZdMlpVFBqOEwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKAcCwQnK0rogFV1yzVV2ipKU+CrfYdOCUMzahhHf8DMvw8FzcaPG2tDZ/7pqkKmbWnd8t5WrFhj5syZbw9Twa74ykr+42/TpqW57bZr7Y3xQ4cOezfkP/LPLvfPFQg48cTWplq14BtI5R4gRQ/whBNaecOy3hKtxvTee5/YKk4p2l26lYYCLVo0I/RVSu/bsmWrzM6duzzvul6AM9O0aNHUns/z5i0qth4owHfjjVeaJk0iVYw0lKeqCJaXdvDgQXsoNWvWsEM9lpfj4jgQQACB4hTo0LiF+e8zrzRVK1cxh48eMW8uzCrOzbMtBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTKTKBUK34V11EGVfnSthXgclW8woZxdNW/tJx/+Eet7w+MZQ70Sl6Vw/bll9/Yo1JlEFX7yqspADFgQC9bWWnBgqV2uKxatWra1ebPX2JUSaVJk4amS5cOoZvaujXHaOgstVNP7ZkQpFq6dJU3tORGs2nTVu+G/yHTsGF9OwzbSSd1Cqz65IYC0/a0jJbXkF5Llqw027fvNKrQ0rBhA1PU/imQkJOzw+h4e/furt0lNPXlm28W275v3vyt7a+Gh9PybdsmVhFYvny1raCmIZKCqtOo+k129hyjijMKnamaVXxTSGLRohV28qmnnuJ5puVpHH9YBXqtSkBXXnmB+de/xhuFEufMWegFOvsEbiM3d4/R515DmOrzqhCIPtf9+vU09evXDVzHP3HXrt3fr/+t9/nfb6v/tGvX1qgynoIWQW3x4hV2OErtS8OoBjUFT1SxTE3batmyWXQxfY71eVbT+acQ4oYNm73zaI03bN0G07RpY++ca29Uha9q1ePv/+7de8yqVeu8ITDX2XOhZcumdtg3LZdX0+dXn3k97tyZa20Usuvbt6epUSO48tLXXy8ye/fut33XMezbt98Ov7l8+Rrv+HfaEI+uDR07npiw+zVr1puNG7daJzdz6dKVdht6XatWDe886uFmxTzqWjFnzgLv/NxubbRvHaPOl/hKcf7z5bvvjtrt6LOgoWxd02fBDeea1/XKrVOYz1X8+6q+bty4xb6vet90rdEx6LwO+2y5/ZeXRw3rqNanTw/73um9UJhT04tzeGEFeBV21rCi+nyrkmXz5hklyujOj9atm5sTTmjtXdeP2fdaw08q4Nahwwkx+y/s94m2p6Zrof9zfdJJHe13oX8nRTnPS+o41L/iPDfkvGTJCu97crl3fcm155WuD+3bt7FDyep6mqzpe2LhwqXe9/q39hqj81KfFZ2X+r1BSxTQ8Mv63lPr1q2jNzxqg8SFvClffTU/+jtPv9+Cmt4393tO36Hu+h5Wcdb/m7Br1w42FK7vBn0PrVy5xlYOVCW8zp3b2eHM3T71e1PfQ7r2btiwxVuvgd1Xr17d7feKW849Bu1H3wMrVqz2vjtXe0Oj77O/h/X778QT27jVCvVYmPPU7WjLlhyvauICOzy2hrZ1522nTu2i33Nu2Yr4eGLDZuaUliearzasNPM2rSoUwa4D+8zkFfPMutwcs+fgAdO5SUtzUtM2XvWw1gnVxpbmbDSrdmwx1atUNYM79Azd355DB8zMNZHAc7/WHU1G7dhrTe7BfWb+ljXmG+9Py/Zs3s4bqrKdaV63oTnkhdimrIz877s+rTqYZnWOn39b9+4yczZGfk+e36m3qexd/7bv32MWbFlrcvblmsu7J/7v3R3e/Kmr5pu1O7eand6xNqxZ25zguZ3d/mTTqFbw7+bVO7aaJTkb7PG5/QQdbNa6JUZ+zeo2MH1aHv/fjke832eTV0Sqf7pj2LJnp3fMa82CrWvMQe8Y2zdsbk47oZtpVb9x0Kaj0/QdMGvDMu89XmFy9u82reo2Mt2btTUnNz/R1KtRK7ocTxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgfIkcDwxUApHFROs8oZfLGxTwEstfohGVfRyoTAXAItfxu3THwAbMfI6N7ncPyq8paYbaQof5afpJpgCKrrppWCGXqvpZtnHH8/wAiL1zP/8z88Db5RpuQ8/nO4N5TXP3jjVjXfXFFJ55ZX3vOG3Eiu7fPpptg1O3XnnjfamrVtHj7q5/dJLE+ykn/zkOu/G3QY79N/evfvsNA3LpeBXUfunG+gKDDVrlhEY/FJYbdy4t21ww+7Y9x+56FivvfYS31TjBdw2mbfemmTDR/ff/8uYeXqxevV6M3585Nh69TrJ/OhH1yQsM3v2N3a4sgYN6tt9JCxQQSboBq9u4OsGz9q1GwKDX/rc6T0Kqu4zYcJHZuTIS5OGS7Ky5tjPln99DRenpjDUj350tQ1WxZPr86tzTeG9sODXkSNHop/jyy4bGhP8Wr9+c3Segoz6vLv9un19+OGnRlX57r77ZhsSmj37azN27FsxxzrPy9RMmvSpHbL00kvPTwhFaVs6rydNmmY++mhGzLqa99lnX5oJEz62+wgKIU6cOM0G0jQkqvdWmH/84z/2/NS6rmkZDZ+ooJ4/pKaw3pQpn7nF7OOMGV9EXzdt2iQh+KW+6v3Uean33TUdu5oCB7feem3MzW3d0HfXC7e8wqb6c00hTBf8UrjupZfesbN69OiSEFTVjMJ+rvzvq87vl19+196gd/1wj7p+aKjZoMCcW6Y8PCp44Sp7KfCl1rfvyTb4pRCiwiRhAZLCHL+u5a4p/FXSwS93fpx77un2HH366ZftMMvqw/Dhg2OCX4X5PtH1SZ9F1w4cOBjzWb/99pHR4FdxnOcldRzqf3GdGwqfPvbYCzZM7lz0mJX1lX2p6/HNN18VeF5rAV1bXn/9Ay/QGvk9YVf6/j8KJI4YcYkXxu3tn8xzT0Ahal2/NQSzAo2jRt2S8JtQts8//7qdftddNyW47dqV64W5X0p479z3goYlHz78nIT1/L8Jdf3Xb8o335wUs9z06bNs6Evfl/ouU+BWnxP9Do1vXbt2tNdfhc78LX4/Cqi9+up7Md9FWv6TTz6zv0f0+y+vkKF/+3pelPNUv1P0/ajvQ//3o/uNrXD5j388Ilr5MH7fFel1m/oZNvi168Bec8RzqxoytHiQyVtelbDnvvrEhq3cfBfYUgjrz0OuN63qNXazvJDWfjP6s8hvClUcO7Fh0+g8/5PpqxeYRz9/z1TzAmLjrv65f5Z5fcFn5tnZH5vjv3qMmbw88rtnWJc+5vpeZ0f3cd8518QEv1Zs2xSdp+DZS19PN6/N/8w77qM2BBYf/FL46pGZE8zRY4lVMdWHe0+/xJzb8ZSY/unFVxtXmKdnRypDaz8KugU17V9huIFtu8QEvw4eORzt5+8HX22yvYDY49kTYzYxxXxjXpw71fy4/xBzYddTY+a5F9rO//7oRbNo6zo3yT6+vuBzU98Lff3xvJEx03mBAAIIIIAAAggggAACCCCAAAIIIIAAAgiUF4Hg/69sih+dwlyqyKXwVnxT+EvzXDUvhcT0lywAtmrlcqPwlz+YlpWdFbj9+P2l02vdFFL1IjWFKvLb/FUWdCPeNVW/UEAhN3e3rWKiagLxTeEWVcRS81dv0c2tMWOeM6quU7t2LaMbegqxKBiiykYKtehGuCo6/fSn14fepFWo4/33p9ibZdpHjRrHh7oqSv+0rWTt22+3mUceedbo+FQ96pxzTvOCO03NgQOHbMWjjz+ebiuv1KxZ01x22ZDophRMUVNFEVV0UNUXf3NWmqaqXocPH0mo6KWqYWoKPlXkpgo+qq62bdsOW8En3kI3mRWE0g1QVehSgEpDvWl53QhVyE7zVXXJhU7821DlNQWGtL6qVvXs2dWur3NAVUsUEHrqqfE2COD/bPu3URzP33hjog19qWKJgmQ6dxRa0nmim+xPPTXOO7aTbKBQ4aWePbsZnbPqp45TFT+mTv3cWp11VuI1U+EjHascBg8+zVYS0018VRHSzfKVK9eaxx9/wbuG/shuN+iYdD48+eQ4e/4qGJqR0cju/5tvltgAhQJkCvAMGXJmdPU2bVpYd1UMW7x4uZ2uwIAL+mjov/j2n/+87gWFFtrh7FRVRdec2rVreu/FWhs6UMWZxx573l4zdC1QU8DVvb+qEqbrYLNmTWIq8hWkal5RP1fumPS+qiqLjllBXPnrM6nzW+/dM8+8bH73u7tjKtS4dcvL49y5C72g4CF7XslATZ9dfc4V6FWoyf+ZKepx+0MernJlUbeZn/V1zipk4r4/q1SpElNFr7DfJzLT51mfG13X9DnWtcA1f7i7OM7zkjoO11/3WNhzQ6Gvf/7zeXtdVMW8H/xgwPdVACvZ0LWuZ6oEqmUUitNvD3/T5+3FF9+0k3Qd1W8IXcsUElTwVtdJBbP1va7vE9pxAX2mFVZ9+OEn7XeGwk8KCrqm34mvvfa+fakhXeN/L+p7Sr8JValLgSt9jyj4qt9YCofqfVOIef/+gzZE7LYb/6hrqr6f9dtK35k6z9eu3Wi/Y/T50HeZgtbqi/apcK/+sYBCk4sWLbOVAFUt7pVX3vVCxNfEbz76eubM2XabCo+qMqW+U1S1Tr/htD+FMuvWrWMuvvi86Dr5eVKU8/Tttz+010z9jh44sLc9fn1+9b2o7z79Zvjb3/5lfv3rn+b7H37kp8/puMwOL/ClVrNq9QKFvt5Z/IV56osP7bq9W7Y3fVt1NG0aNDFrdn5r3vSCRapQ9csPnjN/GXJDNODV26tq1dirkqUqW5+unm9u6D3Yrh//n+mrF9pJ/dt0NnWqRyora8J7S2abZ7zAlVrLeo3MOV7oSo/rd22zYauJS+eYfYcP2fl5/ed9b1sKXrlWt3rsNVBVvv4+0/vHEt7v3m7N2pjTvepazb1qWVv27DAz1y42i7eut/OreL9XVP2rpNqMtYvMtJXzbWWvvl4Fs/o1anvVx3LM514fVN3sn1nvm3aNmpkezWKrZirE95dpr0ZDX6oc1s97j2pXq2HmbV5tZm9Ybn4z6XnTol7Dkuo620UAAQQQQAABBBBAAAEEEEAAAQQQQAABBMpMIC2DXwp3JWuumlf7Dp2iiyn8lZ09y9wzalRgoGv8uLHRamFaScuHhcuiG02zJ3v27I1WAVBgJr9NQzm6pmFtXFP1AN0w080k3WiLv5Gn5VQRQTfUVPXAhS80XTcFFfpS1aRf/vIOG0rRdDXdzNawX3/96xM2XKOKQ6oWFNR08003vs877wxz+umn2tCIq7BQlP4F7cs/TTcNdUNSAQVVkFAIyTUNwachjP7+96dtRSMNMaXqPmrqk0I1clTAIz745ao66f3RTURVWNONSdd0rAriqFX04JcM6tSpZQMPqhzkb6oAphv4Cm3pJrNu9LrPhZZTUO/NNyeaadOy7XIaOlFDsbnmKq9p/aAqI2ec0d+GxmbNmmsrgilsETYcottmYR8VNFKFmj59jt9k0+dAVakURFAATX+6Af2Tn1wfcy4NG3aWDSiqksoHH0zxrn29bTjS9UVV8XQMatqHq+an1wpiKlylAJJuuutRN4yD2sKFy+xQi7pR7g9sDR++2zz66L/tzfSPPppuz9E6dWrbTShUoT+F11zw6+yzB0XPlfj96BgV+lJT1R1/2E433hWqVAhOxzR37iJ741vL6jhuvPFKPfWOY6k3zNhBW6Ut7JpiFwz5T1E/V/7NqirL9ddfHnMcmj958kyvytpHNjCnYO0ll5zvX61cPXefPf93gw5Qw4sq+KXhHosz+OWqvCkYoaBGaTVd1xX6UmU5BUF03VfQz7XCfp/oOqS/8ePfttdBhVzcZ91tW4/FdZ6X1HH4+6rnhT03XnjhDXs9UTXMe+/9Ucy1UNeIgQP7mAceeMx6TJnyubnwwuPVoxQCUnBG7cwzB8b85tA1RJXC9L5pmf/85zVz3333lOvwjL778mr+71QtK58LLzzXhpAVyO/Ro7M3rZndjL6vFLxyy8RvW9/FCn3pM3znnTfFVMBUhTW563ejqmkOHjwo5r31b0vfl6pAqcC9/xxTEEvniX57qeqYfrOp6pj/d+uhQ+d533Mv2aCUQtP6zgmruqjvLP2uu/76K6LVItUP/RZVaEwVY3X91pCP+Q0JFuU81fvlho5WdU99hl3TsOP6zaPQl36v6/tcv5cralNwaNGWSDWoExtHPp/5sdh3+KB5fs4Uu+hQr8rWPYMujq6W2barrYL107efMBom8TWvQtcvTr/UztfQiqqApYpT01YuCAx+7fSCaF9vXmOXP9c3HOTm3TvMY17ISU0Vsn5z1lUxlbRu7DPYPDHrAzNh0fFqqXbhkP+8vcirfOeFoO4YMNRuT4Eq11SF6+HpXtVY77N0efeB5rZTh8T8br6i+yDz1OxJ5u2Fs+xyqmrWJaOVW71YH6es+MZc1O1Uc3v/oV4wr0p02wu9Kl7/7QW3VK3s6dkfmr8Pvy06T0/Gfz3NfLE+8g8Jbuo72FzT8wfR+cO69DXrduWYX018zqze8W10Ok8QQAABBBBAAAEEEEAAAQQQQAABBBBAAIHyInD8zmMpHJG/QpeGZCyt5varil6q7BVf3cv1Q4EyVQZzTVXDylM7cuRo9HCqVMn/W++/eebfhjam4IaaKjIojBLfVGVATVVt/GEz3bxT001r/3Q70fuPKnEMGRL5f9i7m/Vunv9R4SgFOHQzXduJvxFZ2P759xH/XIE1VVBQu+CCs2NCX25ZBcIUXlPTkGX+pmGE1FzlLjdvy5ZvvYDMNlv9Rjfp1FwQzC2jKmgu5KQqVLRggYULl9vPoyp86bMR/7nQ64svPt9+ZvS51fL+psofupGq9YcOPcs/K/pcgTCdG6rKpuVLqummtz/0pf1ov7qx7Q+bXX31RQnnks4Jdx4p2LBp09aYbuo81HHq5rQ/9OUW0n50E1lNQ2MpPBrUtNzIkZfFhL60nCoODRt2tl1Fw2TppnNhm47lv/7rNhs+84e+3PYUkNRxqCkwWRKtqJ8rf590bQo6Dp37TZtGQkkKApTXpmu3ghVq/fodDzXqdd++Pew5q+thcRko7OCCgwo0qiJUaTUdq/Z5xx3XfV+B6vj3b1G/T/JzDMV1npfWcRTm3NB12P1W0DCaQb8rFPZzIUM3/J3zU3hdlThVNVGB2aCmiokaRlqh7+L6XAbtp6ynPfnkWC8498c8/4LCYa6al4xeeOFNW6FSoSsNfeyqggVVWFSIStf3X/3qJzGhL2eh31rue3zJkvDru4ZzDRrWODOzjw0nu+2dd97pMaEvTdd7f8UVw9wi0ZB9dILviQJq11xzcUzoS7P1XXj55cNstS+9VvWw/LainKcKtOk7Xi1oWGbZ6xqkSpjLlq22y1XU/7zghbdy9uXawx/aqU++GfR5/5M3TOD/G/4jc1fmhQnrZdSub4Z37Wenz90Y+xlVlS61jbu322EO7Qvffz7zKll95w2tWNer9KWKX64t2LrWDu9YpVJlc2fm8JjQl1vmjgEXmF4t27mXSR+/3ZtrHrrgZnN+p962ipZ/4S83er+bvT5ouMqb+wb/br7Fm96sTgO7nJYvqdaqfuOE0Jf21b1ZW3Nup4jlspzNNgDm78NX37t3zmhprvaFvtwybRtkGA2FSUMAAQQQQAABBBBAAAEEEEAAAQQQQAABBMqjwPG7j6VwdAMHDsj3XhTS0l9xNFX5UkWv+ABYUPjMX02sOPtQHMeRitvQDXvdjFMgJP5m0uHDh21lAfXbH3DQzSlXOUyBsLDmhv5SIEpVw4KahgVLVrmgMP0L2o9/2ubNOdGX7duH99+FUFQ9yt9cpa7lyyOhBzfPhbw0xJT+5LpgwZJolTYt59bRjT1VDqMFC7hAiSqvqbpPUNPNZzf0pqui5pbT0IFqeq90wzSo6bOnoay0jIYCK6mmqilBTeFIVU9Rq169emhlEv85tmXLtphNbdkS+SyrKl1Y04103eRWUxWmoKZlwiooufNY62n4yMI2DQGpbQXd1HbbbNEiwz5177+bXlyPbruF/Vz5+6EKQkFN572Co2pF8QradipN07B6upGu99NVBXL9q1+/XjSU4aqCuXlhj6rkpopC/j+dlwoBqqLW00+/ZFdV9SZ/uCNse8U5XWGQsCHfivp9kp9+Ftd5XlrHUZhzQ9X4FDZS0/kZ1lSRSddshVL37t0XXcwZ6VrmqhJGZ37/RN8Fbdu2tK/iv9fjl62or3X9uu66y2zASNUcde6puqaaG9I7yEbVUHV9DwrsaXl9x7lhgN11OGg7yb6z/d+Fqr4X1Jo3bxodAjTZ9VdB47DPiQLZ7jNYkM+J+wwW5vtYw2OqipmaAmQuBOY/RlVPfPDB39jqnv7p5e35nkMHzO6D+2P+NuZuN9nrl5rfffyirbylY1b4SX/5bRp+sVvTNvZPQaygdoIXLFLbtm+3HfbRLdO+UXPToXFz+1LDPca36asj/0jmzPY9YipcKfil1twbmlDBsqBWyZvYv3Xw5zl++cEdTzbqS1CbvyXyv0n6t+lkqoX87q1eparp1zryj1dc34K2VdRpGp7RX+nLv71uGZGAv0JqCtK5dtgbUnnFtsg/Lji7fU8jl6DWrWlbL/RWK2gW0xBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgbQWCE5ElNAhaejE0eZRu3UNpegPWWmiglmquKV5aqrMZad5wa2iNm0nM3OsDZNpu2phwzn6+5CVnRUNjBW1D2W9vm7IFbXFb0JDKnXu3N7eXJ8zZ37M8IMLFiyz1akUvFGlAddU4cQ1VVjYvXuPexnz6AI3CgeoUlDQkDsdO54QM5xPzAa8F4XpX/w24l+7/qtygsJt+gtqrhqT+q6qUrpprqYbk2o6boXadKNR7ZtvFttHDQvUsGF97wZzK6Ob2aos4m5Yuiphbht2Bf4TI6DPy+rVkUpJGRmNYubFv1B4S80tr+eR9SNhvbCb0FpO7aKLzo08KcH/qupYWFNARk3DsYad3/V9AUH/zWAdp7uxrWBX2Hmo7derV9feSF63blPM0KOap5bMWeu65t+/m1bQRw2xtXbtRm9ou51eKK+yHcrRBcLcNUPVf4q7FfVzFd+f/JgVh1f8flPltYZxVHMVmOL7pemqlqiqkVdccYEXakj+c+W5516L30TCa31ONByqCzImLFBCE3Q9d9ea+F0U9fskfnvxr4vzPC+t4yjMueEqcOn3hv+aF++hIXF/9rMb4yfboac1sW7dOkmvhQrcqinUVF7btdde7P3eapfn4YV95+h7U+FKDa342Wdf2u0ozHr++ZEqrmEb1nVbVfkUgNLQqHofNZx4p04n2t9y+bm+5+f7UvtPvlxdGyBNdv0NCzq7Y3OfYVXKVCAxLIDuli/qearfl6pi9sEHU20F3sWLV3rv4Qn2d7N+O59wQqvQELvrQ3l5vOHVv+d5KBpG8KcDh4eGg5JtYNWOLWaOV1lqy56dZt+RQ6aDF6Tq1LiFOblFO1PFNyzhwaOx/9tAVb9Wbv/ITFu1wNza7/zobzYN8/jN98M8akhIf1u3KxKWb163gX9ywvOuTSNh8YQZcRN6t2gfNyXy8pj3sHBr5Hdvi3rhvzm1dEtviEe1RVsiy9sXxfyfZH1oVKtOdG/7Dh3/R0GrdhyvAKaKYWFN/0v4JK9yWPa6pWGLMB0BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEhLgeR3Uov5kCLhq4HRSl6quBUf/tJrGxDzhll0FbeysiIBsOLoTnwf8tpmdvasvBZJm/l16hz/F84a0ii/zT+8W1CFAw3LpKoqGu7xhz+8MHpzSUEwtZNP7hJzo/3AgUPRXf/zn89Hnyd7Ehauatw4+Q0KbbOg/UvWD81zQy2qwszvfvdwXovb0Jc/+KWwjqrc6IagqqQp+LVr124bZlHVBhfyUgBMwS8FwjRN2/BXospzxxVgATf0qAvV6ZB1A1XDCqrldbPVzXfvqdbR+u7z5uZrelk1/7HF98EFMd0N8fj5eh12c95/nB98MMW7YTwlaPWYac4lZqL3ItnQsa6P8esU9LXOl7Fj34oJXOjYdBxqmZl9QysDFnRfQcsX9XMVv838vK/x65SX16qwl5Oz3X42w4Jfvbyh31599T0bONQ1sG/fkwt1+AroNm/exFu/pznttH4xw6MWaoOFWClZyMRdewr7fZJXd4rzPC+t4yjMueEcdS0Mu+Yls3LrK1ian+/1sGthsn2kyzwF5vMKNuV1LBpaUdX6XHWua6+9JBp+D1pXAc833pjoVYPdHZ3tru/6BwLDh59jfwNFZ4Y8ye93UbLvTO9bM2TrxyfnFR5VhTI1/W5ToC2v3xLFcZ5qWOV27dqa996bbH9PLly4zBvGepnth6qBqcLp6af386qR5a86lF2xHP2nfo3aRkMAqsrXme0iQ7EX5PD2HT5o/jbzbZO1ZrEdflHrVvZ+g0z+/jdIj+YnmF5e+CusqQrVs7Mn22pg871KXj2bn2gXnblmkTd04jE7xGL3ZrFVhI99v6fKIRXG3L6qVc7f/5zXMI5BTdWzDh6J/G5WVa9kzc3f74XeSqpV/f4fygRtv1LI+an3x7XaVSPnn3sd/1iXil/xJLxGAAEEEEAAAQQQQAABBBBAAAEEEEAAgXIgkPz/u1sCB6hhFxXkUnOVveLDX/7qXKN9AbAS6E7gJtUf17fiGm4ycEelPFE3omrWrGHDEd9+e3x4jLy64V/WVRjyr9O7d+QGvYbYUgBMN5V0E1UVv9T8wzz619NzBaDc8DTx8/yvw26y1a4dGYLOv2z886L0L35b/te6Oa2qXPlp8TeiVbFLQRYN3XjGGf3tkJi68achrtyyp5xyknn33cle8GuJufTSIUbVlhQO0A3L/FTjyE+/0nkZeSk8oqZhu4ratD1nX9Rtpdv6qtDir8wV1n831FbY/JKarvDpU0+Ns1VgVDlJoQLdxNbwYBpKSze3VUHKVS3Te5kqrSJ/rsLeAzd8o863J554MWyx6PTs7Ll5Br9uueXq6NCtbkVdoxUaKevmqkQl60dRvk+Sbdc/r6jneaoch/+Yivu5jrFp0yZ5bjZ+eNI8V6hgC6iaqSozuqahXfU7JqgpCPrCC2+Yo95QbapOpTCoru/6hwZLlqzwru/LzTvvfGznR9ZPnet70PEUdVpRzlO56U8V8BS606P+NKypArT6Gzx4kLnssqFF7WbKrj/uml8YF05ynVQwKmz4QrdMskd9jz84/Q0za90yU6tadTO8Sz/T1xvysIcX1NqQu818uWG5mbpqvhk379PoZuJ/hjSuVdf0adXeW3aFV/VrfjT4NWPNQruOqn3FRw5rfB/COvB9KCu68bgnB/IZwqoXEngKC1PF7Sbhpc7E+D4nLFRqE473JO8rRN5LlFq32RECCCCAAAIIIIAAAggggAACCCCAAAIIIFBMAqUe/FKoyz+UogJWqqo1ftzYhENyATBVBnNBLC2koRoVINP8oGYrhXlDNNISBTTczpIlK23VHH8VqsQlj0/x37zT+vFNFVV69uxqvvpqvve3wAa/5s9fYqsm6cbdSSd1illF4TPXRoy4xBuCJnGbbn5xPBa0f3nt0/W/atUq5uc/vy2vxQPnd+3a0UyZ8rl3Y261na+bcWoKe7mmIamaNcuwQ1Bt3vytFxJbbWe1b982JcIMrp9l9ahAoqvs5YZVUl8UoNCQZDt35tqbncn6p5uharrR6kJfWl8VT3btyvWqDe1PtrodDurAgYM2vKhqbfHNVSSLn67X+/cfr04QNL+kp+k4FbpU5RqFD8899/SS3mWhtz979jc29KX+3nnnjfb9chvr1KmdNxRYOzNoUF/z8MNPlVjVr6J+rlx/K/qjPm+q7qOm7yBd2/JqChSrKmKygKcCXu7anNf2Sn9++E1m1+eifJ8kO57iPc/L7jiSHaPm6RqudujQIRsQCqvopACHhhFU0+fJLefeBw0Hescd19n5/KdwAgpwvfDCm/a7pWnTxkbf1fq906NHF3utjt/qJ598Zt8zDeuo67t7T7Rcr17d7V+3bh1MfoZzjd92Sb3WcJTJmq5XagoSus9WsuWL9zw1Rr8T9efaunUbvfDcZBuk03uhcF1+/+GC20a6PNbyqj3VqFq8gV8N76jQl9q9p11iftDu+PD17b2hHvV3cbcB5leTnjPLcsKHgT23Yy8b/JqxepE31OQFJveAF8j7fpjHc+KGedS+Ojdp5Q0rucpsyk3+j3XW7Mz7e1Tbiw+jaZqaKpdl1K5vcvbl2j5Fpgb/N/dg5Hezqocdj1p5y/peHPauAfHhO7e1PYcOuKfF+uivZrZj/56k287Zm5t0PjMRQAABBBBAAAEEEEAAAQQQQAABBBBAAIF0FKhcFp3WUI7+pqBW+w6djAJeQU0VuFatXG4DY5qv5RX+Clpe0zTPHxQL2mZFndanT2S4rO3bd9oKOXk5KFjzyScz7WKqcKEKO0FNwymqKcB05MiR6I39Pn16xNzE0zKNGtXXg23+amJumntUKGDnzl32T8+L0grSv7z2o1CRmmzczb2gdXQDWv3P9Q1d5JZTVQvd3MzN3WPWrNngVUpb5Q1BVsN06dLeLWIfNdyjmlxd8EvVwmjGG87okyiDPmf+Jl+1vEIlbr4bXtNto1OnyPrJbu4qQDB69LPmj398xEybFhs0dZWxXMDAbdf/mGzb/uVK8rk7F13ltLB96XOuz7IL2oUtV1LT16+P3Eht06ZFNOARv6+mXpWezp1jz5/4ZYr6uqifq6Luvzys//XXi204T0GHO++8yQvP/jj07957b7UhV13/VTGoPLbi+D7Jy6U0zvPSOI5kx+nOTV2Xv/12W+iic+cutNfsP//5H/a3ilvQ9T/ZbxItq7CwroWqcEoLFpg06VOvQulG+5vmZz+70QwY0NsOyauhehWUjm+qZqrWvXvnhN+LbtnevXvEDBnuppfVo6qUJWuq6KqmfyzhQuXJlte8opynmzZttdfIBQuWBu5GIa+bb74qOuSk/x90BK7AxBiB5ds329cacnFg2y4x89wLhc0Gd4j8byE3Lf5xUNuutmKYwlNzNq40M9dGhnnUEJRtGmTEL24rimnidi/ING/zqoT5mnDo6BHz7pIvAucVZKKGqlRbsyt5qNGFzLo3Px4s1HpNazfQg21b9+50T2Mej3x31GzZHTwvZsFCvGhZr5FpULO2XdP1MWgzR7zfE6t3bA2axTQEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCCtBcok+BUmprCWQlthwysqAKbKYK7Sl5Z3gTEFvvRc09RUVaywzb9/t6/CbivV1uvb9+RocOL996ckvUGqm+1vv/2hDSfpOM47L7wikKp61a1bx6tidMCGvhYtWm4PvX//xJsgGi6yTZuWdr6G8AprCvb84Q9/N3//+zP5vnEWtq2C9C9sG266htFxFSnckGVunnvUzefHH3/R9v+ttz50k6OPqk7jwkYTJnxkq110794pelPOLegqgOlm9YoVkRuNqhZWkZtsVbFi7txI1SC9t82bN40hcbYKmYSFmrZu3WaH2NSKbnm3Efd6/vylRiHJoKaqdi445gJ6bjmFkNQUDlCFvaAWHxYLWqakp3XvHrmBOW/eInvuBu1P5/J99/3NfpYVeCju5q+Gsm3bjsDNu6Fe9+0LrxShz8WGDZGbs4Eb8SbWqhWpNhi2n7D13HT3uSjs58ptpyI/akhONVVpU9BVwYiwP1Ws0fC3am49+6Ic/ac4vk/y4iiN87w0jiPZcTZr1iQ6XO2nn84KXfTjj2fYefrsKWztmqpRqSk0FhbqUQD2/vsftddCfSfTEgU09O5HH023My6/fIj9vXnFFcNsFU59l77xxgcJK7khu/X7Maxp3aDQWNjyJT1dQSv9Bghqa9ast8Oea54qyOW3FeU81dCaL774pnn++ddtJcWgfeq7VoFbtRYtYn8zBS3PtOMCdatHrhXfHfvO7DucGF50S674PiDmXsc/Khx2xomRamEaGnL66sh1JCww1t0bSlJDRKo9MvMdszRnQ8wmFQj7v9NeMxt2hYddY1ZI8uLk74Nfn69ZbDbu3h64pIa1zF4XCRee7PXN31rXbxx96Y4rOuH7J+8s/sIc9QxLqnVrGjnf3l/ypdl/+FDgbt7zQnK7vEprNAQQQAABBBBAAAEEEEAAAQQQQAABBBBAoLwJlEnwK8s3DGN8sMpV8woLgGl5hb/iA2Au8KU3SKEvhcQK2/z9K+w2UnW9GjWqmxEjLrVBKg1l99BDT3pBuzkx1SsU+FKA4h//+I+ZMSPyr8h79uxm+vfvFXpYupnkqi69+eYkW0lDQ/y0axf7L8LdBoYNO8vegFqyZIWZOHGarQbh5inAoRtqkydHKo2dfvqpRQ5+FbR/ri9Bjxq+8qyzIsOM6gZn/M2/w4ePGIXqdPNYlR7U/6DmKne5Sl4u5OVf9oQTWtlhB1Xx6ODBg3bYIE0r702V0HJzd0f/FDhasWKN+fzzr2wQ8K23JlmC+vXrmpEjL0vg0PBQ+vxpyKmnn37J6Catv6kaxzPPvGTnK6TVq9fxITa1nF43adLIfo6feeZlO9ymf/3Nm7d6wyZ9bCdpSM74IZMUBKtWLTKS7iuvvGtc9Q+tcPDgIfPaa++bhQuXRW/C+rddms/PPHOAF5aoY6vYqBpL/I33rVtzzLhxb9sudenSwQ49Wtz90xBtVatGrBSkVFBON/h1HrnWsWPkBqNubn/55TducvRR16wPPpgSGtJzC2roVDVV2NNwg3v27LVDw7n5eT0W9XOV1/bL+3wFZzRso1q/fpHqk3kds6vWqPde1RHLWyuu75NkLqVxnpfGcSQ7Rs0755zT7CL6ntDvB/2WcE3XiEmTptlhrjXNfa7cfFWbciGdl16aYPR58zdV+ho37i37W0nD9ylEX16bhknWtT+vv/gAraqcKnwkawWyBw3qZ4kU3NXvTjWF/b/+epF97v7jArW6tgdVwlSAWt+Z/vfTrVtWj+rLs8++Yr+PVOXWNf3ue+aZV+xLnRMaUjG/rSjnafv2J9h/kKDvzpdeeidhmGv9FtLvUr1H+scXzjy/favoy3XNaGOqVq5iGcbOnRrI8fnaxWb6qsg/SAhc4PuJ53aM/IOYz9YsMgu2rPWGWaxszm4ffD2p4wXO/jL0BlO/Rm2zZc9O8/P3/23ueudJc//UV83PP3jW3PrGo94QlEvNZd0j/5sk2X7zmnf6iSeZVvUaG1Xlun/KK2bNztjfzaqSdf/UV+z8Vl7I67QTYn83t/OGu2zXqJndzZsLs6KhNk3Qlfi9JbPN83OmhA4BaVcs4n8uPWmAfZ9UUe2+yePs0JX+TaoPz3452Q5t6Z/OcwQQQAABBBBAAAEEEEAAAQQQQAABBBBAoDwIRO62l+GRDBw4wIa4VLHLH95SACwr67rQEJcCYFnZA2KqgxU18OUY/P24Z9QoN7ncPKrSxW23XetVBnjDhonGj3/b6E9DHemGpioh+W9k6QbnyJGRm3bJEFTda/r0WdEbTvE3Vv3rKkh2001X2eoECmzMnv21F55paYMwixevsIEMLa8w2fnn/8C/aqGfF6R/ee3k0kuHeDc3j5mpUz83//rXeK9iTRs7DObu3XtsqOTw4cN2E1deeYFxQ1DFb1MVUt59d7KdrApiuvEc3xQc69mzazSAp6Hs8jtsUPy20un1H/7w//LsrkKFGrpI4a/4pjDTXXfdbMaM+bcNfT344BNeVbAMG+bSzWrdXNbN6YyMxubuu2+y1er826hXr64ZNUrrP2eDAg888LhdXyEl3RRXkEvrt27dwvzkJ9cnvCda7rzzzvDCSFNtxbG//vVxW/mkevXqtqKM1r3uusvM669/kBC28vejpJ8r3Hb33Tebf/7zeTucqKrZKPwgUwUSdS1Qk52sS6IplHn22ZlGlXgU0HvsseftbnSd0lCAahrm65RT5tvAgCqaaFmdL/JXCG/BgmXWWVWkXJDSrhj3n3PPPc0Gj3R+Pvfcq3auzqdHHvlD3JLBL4v6uQreasWZquu8Pvu63gUFXYMkFCBRgELBGwUDVR2sOJtCJb/97UMxm/QHTBSO9l9zVSnn17/+aczyRX1RHN8nyfpQWud5SR9HsmPUPAW/9NtF1UJVSVOVITU8td6/TZu22Oqleq7v5fhAjq5DP/vZDebJJ8fZkLHed4VpFCBWEF6hQ30udA2/447rjL9SYV79Srf5L7/8Tr66rCDtb397V3RZVTdVxbRI0OuS6HQ90e8dheBnzpxtg0mq5qfvWbWLLjrXqLKkvpsfeOAxG0pSBba6dWtbe4V0VZ1NVd3cd5JdsQz/c8YZ/b3vnaX2N2y1atXs79ft2yNDk6tbkc/JSO/zE6n+mZ+uFuU8bdCgnv2d/sILb3jhujn2WqnKuvo+V2VSVWLT955+4+v3vP+alp++VfRlmtSuZ2479XzzxKyJXoDpS5Plha0GtO1sejZvZ3L27jILtq4zX6xfZvq36RytiBVm1rNFO9OsTn2zdW+uXeTUNh1Nw5p1whY3JzRoav485HovsPSxWejtZ+X2LfZPQbSuTVubH558mrd+XfPGgtghx0M3GDJDfXhg2I3mVxP/Y4dCvGvCU6ZtwwzTvG5DGzpbtzPHVuvSkIp/HXpTdFhFt7nK3rX1jgFDzW8/HGsOHjlsK5H9M6u2aVq3vtm4a7vZf+SQGdalj9l1cL9RVbGSaL1bdjD/dfol5uHpb9pQ3c2vjTGtGjQ2DbzgnIJrLShY8QAAQABJREFUew8dMCc1a2s6N2lpJiyaVRJdYJsIIIAAAggggAACCCCAAAIIIIAAAggggECZCZR58MsduSp0ZQ7MNKPHjIkJcymElZ09yyiA5aqDxYfEiivwpb5o2/7m9umfVh6ea/isX/7ydntjVDfjVYVIgRb9uaZgjSoQxN8gdfPjHxV+0k0u3fhTSxb80vzevbt7N/Oq2T6sXr3BfPXVfE22rUGD+mbIkB+Y007rV2w3qAraP9eXsMfLLx9qw3LqtypyaXgf11SV68ILz/FudnZykxIedVPOBRpU/SvsRrICEq7ymqsSlrCxCjBBPvpctGrV3AwY0MveSNbN+rCmIKNCTQpX6QatQkWu8pfWU/hQAQBtM6g1bNjAru+qcyns5Sp3NWrUwBumroPREFZh79uwYWd7265nVAFP55eGqtLNVgVHVDFOFfTUt7JuGiZz1KhbbFBi1ap1MRXsNCTpD34wwIbYdMO4pJrOFQ0Vq2CPhuY8dCgSnPTv78Ybr7DXClXy8b8XqmKo6iW33PJDW31Fwa+wm9o6H2+/faQdikzhP4V+CtqK+rkq6P7K0/JuaFyFQPL7edK5qgCwroG61uq66yrEFZeNwmhhTWEffxBMgd+SaEX9PsmrT6V1npf0ceR1nEOGnGk/WwqH7tixyyiMrabPjMJGCoeFhQ4VLlKQV0NcqyqgqtO5CnW6pug3y/Dh59ggbF79qGjzFy1aZkNdOu6w71UFAxXsV8Br/PgJ9lqs5RUUu+eeW2xFKg3pqmu4C/AqHKbw+2WXDfWqd47/PvhVSauVaVMYWr9RVRFTw3D7hwdV2PCKKy6w/yCgoJ0synmq39wKbX/44XSzbNkqs27dRvunPuizrbD01VdfZPT7hVZwgUu8alINa9Ux//nqE7Np9w7zwZKv7F8Vr2JXh8bNzf+cN9JoKEc3FKJ3yQhsmjzYq/r18tcz7PzB7fOuCtexcQvz5/Ov96ptfWeHezzghag0DGRNb39q8UNA2omF+E9G7fpeqOtG87gXcPti3TIbllJgSk3HmXlCV/PTAcOMgnBBrVeL9ubBYTeZh2e8ZTZ7Rqq8pT8F3YZ06W1+fOpQ8+dpkdB90PrFMW1wh542oPbinKleuG6XWe8F1vS/zjRk5hmde5vbvf6/Nn9mceyKbSCAAAIIIIAAAggggAACCCCAAAIIIIAAAiklUMm7oVkydzGTHKY/uBUU2lK1r/gAmDZnq3x581zTugqLhYWz2neIhG78w0JqXf8wkv55/n5puaC+aXp5axr6RVUUFPpSQEXBhiZNGnqPpXdzSB9DhTk0xJ8qbKhiUrJQT6q9B6qksHbtRhsQUP/DwkSp1u+K0h9VC1Igcc+efbaKiAKKCt3lt2l9nSMaCrF16+YFen8VKtGQWTq/NCRkQfab3/4V53IKqKlCiK4D+iyrmkkqNVX0UXBg27adto8K0pXVtaKon6tUcqUvqSNQGt8npXGel8ZxhL1r+k2h4Rp37Mi1wSJVFFWluYI0nd/r1m3y1q9hQ+35DSsWZB8sGyugoQoV/tVvQV3b9VswVZq+///7vx+w3VG47cwzI8Pr6Tt+/frNtiqhfju7IYWLo9+FPU/12dV3pIKP+r2j7/KwQHRx9NNtI2dwF/c06WPGlKVJ56fyTF1bFCjatHu7DXp1bNyywMMXPj/nE/OSF/yqVbW6GXvNL6IBrsIe94w1C81fpr5mV//b8FvMSU3bFnZT0fV2e5W5NuRuM7sO7PWqe9Uxres3MfVq5P8fAGhoyvW7ckybBpGqYdENl9KT77z3ScNV5niV1TQ0pfpf1q0inB9lbcz+EUAAAQQQQAABBBBAAAEEEEAAAQQQqMgCVcv64FXRS9W+/E1BrszMsbb6lua7pkCYa3mFsuIrd7n1wh7jQ1/qQ3y/wtZN9+kakkbVp/RXVk03pDRsW+viHcWr1A5H4ZiwIR1LrRPsKFRAYauiBK60bvv2+Q+K+TuiUFKLFs3sn396qj7XjfZUutke76TKPaqKor+ybkX9XJV1/9l/agqUxvdJaZznpXEcYe+gflMU9bqr81vV6WilJ6AKmmX5W7QwR6rveFV5LYlW2POU76aSeDci29S1RcMf6q8wTf/aasrKSIXj09qdlDT0pUpek5bPMZW8/7u8e2ZoeOmTld/YrihIdmLDZoXpVsI6Cnl1a9omYXp+JxTFKL/7SLachp5s36i5/Uu2HPMQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHyIhA+TloJHmF8oCospKXlVq1cHlPRS4EvTYvfhuuutqVKX/7AmJsX9KjKYvGhLy2noSVpCCCAAAIIIIAAAggggAACCBRVYNHWdUbVsNTO84Z8TNbaecGlbG/IxfeXfGn+1wf/NnM3rTKHjx6NrqKqXI9mvWuy1i6x037Y83RTu1qN6HyeIIAAAggggAACCCCAAAIIIIAAAggggAACCFQcgTKr+KUAV37DWRqOMa8WFN7SOtpP2FCQmq8qYv5KYprmH/5Rr2kIIIAAAggggAACCCCAAAIIFFZg6qpIda5mdRqYU1q0T7qZ6lWq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)"
      ],
      "metadata": {
        "id": "Ai5Q0fbVhbOM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Further details on how to use Playground will be shown during the Lecture session and will focus on the following aspects -\n",
        "\n",
        "- How to use GPT-3.5 with the System, User and Assistant messages -> this is what we will also use in the project section of this course\n",
        "- Check the use of text-davinci-003 using Playground -> this works only with the text completion API which will soon be deprecated but it helps to serve as an example of where the journey started\n",
        "- Briefly check the use of Curie, Babbage and Ada models using Playground"
      ],
      "metadata": {
        "id": "gyJUDjRmcK5M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Making API calls to OpenAI\n",
        "\n",
        "Before using the OpenAI API, you need to get an API token. You can login to the same account that you created in the previous step and navigate to the `Manage Accounts` section of your profile and look-up the `Usage` tab. By default, most new accounts automatically get \\$5 of credit to try the API and this should be enough for the purposes of this project. If you notice that you do not have any credits, this could be because the email or phone number associated with your account was already activated earlier or that your credits have expired. In this case, you might need to setup a payment method to start using the API. We do not expect costs with this project to exceed \\$5.\n",
        "\n",
        "After that, please visit the `API Keys` tab and Create a new secret key and copy this and keep it available. We will use this API key in subsequent steps while making the API calls."
      ],
      "metadata": {
        "id": "wnAcQD1pidNv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Screenshot 2023-07-25 at 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)"
      ],
      "metadata": {
        "id": "zxCXua310tLk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We need to install the `openai` library which is the Python package that allows us to make calls to the API. While we can use the API and make calls directly, it's much easier to work with the Python library provided by OpenAI called - `openai` 🤭."
      ],
      "metadata": {
        "id": "DK-yU88QiwD2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HvetiiXwToxO",
        "outputId": "5a5088fe-b9cd-4960-f587-e4453da6965e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting openai\n",
            "  Downloading openai-0.27.8-py3-none-any.whl (73 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m73.6/73.6 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests>=2.20 in /usr/local/lib/python3.10/dist-packages (from openai) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from openai) (4.66.1)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from openai) (3.8.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai) (2023.7.22)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (23.1.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (6.0.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (4.0.3)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (1.9.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (1.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (1.3.1)\n",
            "Installing collected packages: openai\n",
            "Successfully installed openai-0.27.8\n"
          ]
        }
      ],
      "source": [
        "!pip install openai"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Running the below cell will create an input text box where you need to paste the OpenAI key that you saved previously."
      ],
      "metadata": {
        "id": "rFtRi-c8kLpa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "from getpass import getpass\n",
        "\n",
        "openai.api_key = getpass('Enter key in cell')"
      ],
      "metadata": {
        "id": "6KBQE-FkT3wL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0fa9a20d-1f51-4716-a94d-69828e90323f"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter key in cell··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the demo you would have already observed how to use the Playground web interface to interact with the model. The following sections of code are meant to replicate the same process programmatically."
      ],
      "metadata": {
        "id": "YJeykJgDcHWm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Predicting sentiment of Amazon Product Reviews\n",
        "\n",
        "In order to understand how to work with the API, we will try to predict the sentiment associated with Amazon product reviews using gpt-3.5-turbo. You can copy-paste reviews from any Amazon product page but I chose to use the dataset from Chapter 11 of my book - [Blueprint for Text Analytics using Python](https://www.oreilly.com/library/view/blueprints-for-text/9781492074076/), published by O'Reilly in December 2020. In that chapter, I provided a blueprint for classification using different techniques with the most sophisticated being a fine-tuned BERT model (state-of-the-art at the time) that achieves an accuracy of 95%. Two years later, I want to see how we might go about achieving the same thing with the help of OpenAI APIs."
      ],
      "metadata": {
        "id": "6_dvKfFrEaLL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -P /content/data/amazon-product-reviews/ https://github.com/blueprints-for-text-analytics-python/blueprints-text/raw/master/data/amazon-product-reviews/reviews_5_balanced.json.gz"
      ],
      "metadata": {
        "id": "sU0hiMaMmCkr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25506163-485a-4e8a-f824-9b93b17b62a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-08-16 15:59:40--  https://github.com/blueprints-for-text-analytics-python/blueprints-text/raw/master/data/amazon-product-reviews/reviews_5_balanced.json.gz\n",
            "Resolving github.com (github.com)... 20.27.177.113\n",
            "Connecting to github.com (github.com)|20.27.177.113|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/blueprints-for-text-analytics-python/blueprints-text/master/data/amazon-product-reviews/reviews_5_balanced.json.gz [following]\n",
            "--2023-08-16 15:59:40--  https://raw.githubusercontent.com/blueprints-for-text-analytics-python/blueprints-text/master/data/amazon-product-reviews/reviews_5_balanced.json.gz\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 16649530 (16M) [application/octet-stream]\n",
            "Saving to: ‘/content/data/amazon-product-reviews/reviews_5_balanced.json.gz’\n",
            "\n",
            "reviews_5_balanced. 100%[===================>]  15.88M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2023-08-16 15:59:41 (113 MB/s) - ‘/content/data/amazon-product-reviews/reviews_5_balanced.json.gz’ saved [16649530/16649530]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "file = \"/content/data/amazon-product-reviews/reviews_5_balanced.json.gz\"\n",
        "df = pd.read_json(file, lines=True)\n",
        "df = df.drop(columns=['reviewTime','unixReviewTime'])\n",
        "df = df.rename(columns={'reviewText': 'text'})\n",
        "df.sample(5, random_state=12)"
      ],
      "metadata": {
        "id": "lEo0TbTnERQ5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "21274949-285a-44d1-c65f-fed492f6ed53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        overall  verified      reviewerID        asin  \\\n",
              "163807        5     False  A2A8GHFXUG1B28  B0045Z4JAI   \n",
              "195640        5      True  A1VU337W6PKAR3  B00K0TIC56   \n",
              "167820        4      True  A1Z5TT1BBSDLRM  B0012ORBT6   \n",
              "104268        1     False   A4PRXX2G8900X  B005SPI45U   \n",
              "51961         1      True   AYETYLNYDIS2S  B00D1HLUP8   \n",
              "\n",
              "                                                     text  \\\n",
              "163807  Good Decaf... it has a good flavour for a deca...   \n",
              "195640  I could not ask for a better system for my sma...   \n",
              "167820  good product at a good price and saves a trip ...   \n",
              "104268  I like the principle of a raw chip - something...   \n",
              "51961      Fake China knockoff, you get what you pay for.   \n",
              "\n",
              "                                                  summary  \n",
              "163807                                              Nice!  \n",
              "195640  I could not ask for a better system for my sma...  \n",
              "167820                                         Four Stars  \n",
              "104268       No better alternatives but still tastes bad.  \n",
              "51961                                  Definitely not OEM  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-b762c134-13b2-4c2e-8e83-e14f87ce4a88\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>overall</th>\n",
              "      <th>verified</th>\n",
              "      <th>reviewerID</th>\n",
              "      <th>asin</th>\n",
              "      <th>text</th>\n",
              "      <th>summary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>163807</th>\n",
              "      <td>5</td>\n",
              "      <td>False</td>\n",
              "      <td>A2A8GHFXUG1B28</td>\n",
              "      <td>B0045Z4JAI</td>\n",
              "      <td>Good Decaf... it has a good flavour for a deca...</td>\n",
              "      <td>Nice!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195640</th>\n",
              "      <td>5</td>\n",
              "      <td>True</td>\n",
              "      <td>A1VU337W6PKAR3</td>\n",
              "      <td>B00K0TIC56</td>\n",
              "      <td>I could not ask for a better system for my sma...</td>\n",
              "      <td>I could not ask for a better system for my sma...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167820</th>\n",
              "      <td>4</td>\n",
              "      <td>True</td>\n",
              "      <td>A1Z5TT1BBSDLRM</td>\n",
              "      <td>B0012ORBT6</td>\n",
              "      <td>good product at a good price and saves a trip ...</td>\n",
              "      <td>Four Stars</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104268</th>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>A4PRXX2G8900X</td>\n",
              "      <td>B005SPI45U</td>\n",
              "      <td>I like the principle of a raw chip - something...</td>\n",
              "      <td>No better alternatives but still tastes bad.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51961</th>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>AYETYLNYDIS2S</td>\n",
              "      <td>B00D1HLUP8</td>\n",
              "      <td>Fake China knockoff, you get what you pay for.</td>\n",
              "      <td>Definitely not OEM</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b762c134-13b2-4c2e-8e83-e14f87ce4a88')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-a8dc8817-7dd9-4d7f-922f-d7a4ac88a4a4\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a8dc8817-7dd9-4d7f-922f-d7a4ac88a4a4')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-a8dc8817-7dd9-4d7f-922f-d7a4ac88a4a4 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b762c134-13b2-4c2e-8e83-e14f87ce4a88 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b762c134-13b2-4c2e-8e83-e14f87ce4a88');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We make use of the number of stars to act as a proxy for the positive or negative sentiment associated with a review. If a review has greater than 3 stars then we consider that the user was satisfied and expressed a positive sentiment in their review. If a review has less than 3 stars then we assume that the user was not satisfied with their purchase and expressed a negative sentiment in their review. In this way we also ignore those reviews that have 3 stars as they are difficult to classify as either positive or negative."
      ],
      "metadata": {
        "id": "tcxw4s-cGBe9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Assigning a new [1,0] target class label based on the product rating\n",
        "df['sentiment'] = 'OTHER'\n",
        "df.loc[df['overall'] > 3, 'sentiment'] = 'POSITIVE'\n",
        "df.loc[df['overall'] < 3, 'sentiment'] = 'NEGATIVE'\n",
        "\n",
        "# Removing unecessary columns to keep a simple dataframe\n",
        "df.drop(columns=['overall', 'reviewerID', 'summary'],\n",
        "        inplace=True)\n",
        "df.sample(5)"
      ],
      "metadata": {
        "id": "JzX4p216GnkR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "a15c3216-7930-4185-95a6-dfe323719bc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        verified        asin                                  text sentiment\n",
              "275606     False  B00YHYGGMG                               Awesome  POSITIVE\n",
              "203820      True  B01DAD0F7Q  actually worked better than expected  POSITIVE\n",
              "40818      False  B00DREUQNM                      pieces don't fit  NEGATIVE\n",
              "179236      True  B000C71I2K                                worked  POSITIVE\n",
              "237056      True  B01GF2TV7Y         Nice product & fast delivery!  POSITIVE"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-b8aafaa1-29ed-4159-af0a-de52dcc6f113\">\n",
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              "      <th>275606</th>\n",
              "      <td>False</td>\n",
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              "      <td>True</td>\n",
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              "      <td>POSITIVE</td>\n",
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              "      <th>40818</th>\n",
              "      <td>False</td>\n",
              "      <td>B00DREUQNM</td>\n",
              "      <td>pieces don't fit</td>\n",
              "      <td>NEGATIVE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179236</th>\n",
              "      <td>True</td>\n",
              "      <td>B000C71I2K</td>\n",
              "      <td>worked</td>\n",
              "      <td>POSITIVE</td>\n",
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              "    <tr>\n",
              "      <th>237056</th>\n",
              "      <td>True</td>\n",
              "      <td>B01GF2TV7Y</td>\n",
              "      <td>Nice product &amp; fast delivery!</td>\n",
              "      <td>POSITIVE</td>\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b8aafaa1-29ed-4159-af0a-de52dcc6f113')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-82a2c8c7-ef18-4872-a02c-9e634c243428')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-82a2c8c7-ef18-4872-a02c-9e634c243428 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b8aafaa1-29ed-4159-af0a-de52dcc6f113 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b8aafaa1-29ed-4159-af0a-de52dcc6f113');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Review classification using a user prompt\n",
        "\n",
        "We will provide a prompt and ask the LLM to classify a given product review into positive or negative. I retrieve the review from the dataset using the variable `review_number`. Please feel free to try with different values or even better, just replace the review_text variable with the text of any other Amazon product review that you want."
      ],
      "metadata": {
        "id": "ezGEAg5hERzq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "review_number= 4\n",
        "review_text = df['text'].iloc[review_number]\n",
        "print (review_text)"
      ],
      "metadata": {
        "id": "aoICd9Q8KGPt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49a67947-2ddf-4c61-8fe3-23f90e99e826"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Constantly rolls down\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to achieve the classification, I need to provide instructions to the model on what I expect it to do. As you can see below, I provide detailed context and information on when a review would be considered positive or negative."
      ],
      "metadata": {
        "id": "x-NPYv5Aqt8c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "instructPrompt = f\"\"\"\n",
        "You will be provided with a customer review of a product on an online e-commerce website. You have to understand the context of the review\n",
        "and classify the sentiment into three categories - POSITIVE or NEGATIVE or OTHER.\n",
        "POSITIVE - this category indicates that the user was happy with the product and liked it\n",
        "NEGATIVE - this category indicates that the user was unhappy with the product and did not like it\n",
        "OTHER - please output this category if you cannot tell what the review is\n",
        "\n",
        "Only provide the determined category and nothing else. I don't want any explanations.\n",
        "\n",
        "Review Text: {review_text}\n",
        "\n",
        "Sentiment:\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "NSDowHYBWOzS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's take a look at what our complete prompt looks like -"
      ],
      "metadata": {
        "id": "8dI2sOY9n_wZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print (instructPrompt)"
      ],
      "metadata": {
        "id": "_GNFVEDCn-ow",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "247605ba-60c9-4157-e6f9-6400b251782f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "You will be provided with a customer review of a product on an online e-commerce website. You have to understand the context of the review\n",
            "and classify the sentiment into three categories - POSITIVE or NEGATIVE or OTHER.\n",
            "POSITIVE - this category indicates that the user was happy with the product and liked it\n",
            "NEGATIVE - this category indicates that the user was unhappy with the product and did not like it\n",
            "OTHER - please output this category if you cannot tell what the review is\n",
            "\n",
            "Only provide the determined category and nothing else. I don't want any explanations.\n",
            "\n",
            "Review Text: Constantly rolls down\n",
            "\n",
            "Sentiment:\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following section makes the call to the API with the prompt that we have just created."
      ],
      "metadata": {
        "id": "tHWD7EPBoKyG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "chatOutput = openai.ChatCompletion.create(model=\"gpt-3.5-turbo\",\n",
        "                                            messages=[{\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n",
        "                                                      {\"role\": \"user\", \"content\": instructPrompt}\n",
        "                                                      ]\n",
        "                                          )"
      ],
      "metadata": {
        "id": "qZrNcapCKZhL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's take a look at the predicted sentiment and compare it with what our original prediction based on the number of stars."
      ],
      "metadata": {
        "id": "4ZWe4iWzoRYo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print ('OpenAI Response: ', chatOutput.choices[0].message.content)\n",
        "print ('Sentiment based on ratings: ', df['sentiment'].iloc[review_number])"
      ],
      "metadata": {
        "id": "ewVHb7rBKn1A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e48169a9-2bed-41f4-ecea-9a4a3b4db03d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "OpenAI Response:  NEGATIVE\n",
            "Sentiment based on ratings:  NEGATIVE\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So as we can see, this is a simple call to the API and based on the response you can see that they match as well."
      ],
      "metadata": {
        "id": "zaEq8CACpD0p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Review classification using a system prompt\n",
        "\n",
        "We've seen in the previous section a simple way to use the API. In this section, we will make two changes to the way we call the API -\n",
        "\n",
        "- We provide the instructions in a system prompt. This is a good way to provide global level instructions to the model which should allow it to maintain consistency throughout the conversation\n",
        "- We provide multiple examples in the prompt to check if the model is able to respond back with multiple predictions"
      ],
      "metadata": {
        "id": "PDMo8WkHpTCs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Appending multiple examples of reviews in the user message, leaving a blank for predicted sentiment\n",
        "user_messages = ''\n",
        "for review in range(10,15):\n",
        "  user_messages = user_messages + 'Review Text: ' + df['text'].iloc[review]\n",
        "  user_messages = user_messages + '\\n' + 'Sentiment: '\n",
        "  user_messages = user_messages + '\\n'"
      ],
      "metadata": {
        "id": "kPH7l8SuqG_L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "systemPrompt = f\"\"\"\n",
        "You will be provided with a customer review of a product on an online e-commerce website. You have to understand the context of the review\n",
        "and classify the sentiment into three categories - POSITIVE or NEGATIVE or OTHER.\n",
        "POSITIVE - this category indicates that the user was happy with the product and liked it\n",
        "NEGATIVE - this category indicates that the user was unhappy with the product and did not like it\n",
        "OTHER - please output this category if you cannot tell what the review is\n",
        "\n",
        "Only provide the determined category and nothing else. I don't want any explanations.\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "2L1r3MAgrrrh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print ('System Prompt: ', systemPrompt)\n",
        "print ('Review Observations:\\n', user_messages)"
      ],
      "metadata": {
        "id": "9loIYUP8r7Rw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60160c05-e21d-4c85-8942-2fac635aa3b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "System Prompt:  \n",
            "You will be provided with a customer review of a product on an online e-commerce website. You have to understand the context of the review\n",
            "and classify the sentiment into three categories - POSITIVE or NEGATIVE or OTHER.\n",
            "POSITIVE - this category indicates that the user was happy with the product and liked it\n",
            "NEGATIVE - this category indicates that the user was unhappy with the product and did not like it\n",
            "OTHER - please output this category if you cannot tell what the review is\n",
            "\n",
            "Only provide the determined category and nothing else. I don't want any explanations.\n",
            "\n",
            "Review Observations:\n",
            " Review Text: They looked very cheap\n",
            "Sentiment: \n",
            "Review Text: Artculo equivocado en color\n",
            "Sentiment: \n",
            "Review Text: I've always gotten this size in Nike shoes with similar description, but these are too long- at least an inch and 1/2.\n",
            "Sentiment: \n",
            "Review Text: After less than a week the sole ripped off on the outside of the shoe\n",
            "Sentiment: \n",
            "Review Text: wrong shoes\n",
            "Sentiment: \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chatOutput = openai.ChatCompletion.create(model=\"gpt-3.5-turbo\",\n",
        "                                            messages=[{\"role\": \"system\", \"content\": systemPrompt},\n",
        "                                                      {\"role\": \"user\", \"content\": user_messages}\n",
        "                                                      ]\n",
        "                                          )"
      ],
      "metadata": {
        "id": "CJUM0BOAKqvE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print ('OpenAI Response: ', chatOutput.choices[0].message.content)"
      ],
      "metadata": {
        "id": "1YqNYG_GL-BO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61f0a55b-32af-48fe-e9a9-69236505022b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "OpenAI Response:  NEGATIVE\n",
            "OTHER\n",
            "OTHER\n",
            "NEGATIVE\n",
            "NEGATIVE\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see in the above example, we were able to provide the model with multiple examples and it provides a response to each of the observations.\n",
        "\n",
        "**NOTE**: To be clear, this may not always work and there are instances when the number of observations and predictions will be different. This is due to the relative importance of different sections of the input context window but we will not dive deeper as it is an advanced concept. This also does not play a big role because you are charged based on the number of tokens and not the number of API calls. Therefore it is okay to call the API multiple times for each observation. The limiting factor would of course be in the time penalty of multiple API calls."
      ],
      "metadata": {
        "id": "UU5uuJJoO1j9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Review classification using function calling\n"
      ],
      "metadata": {
        "id": "pISYyRT8PflU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "One of the recent introductions to the OpenAI API is function calling. If you notice the prompt that we provided in the previous sections we had to explicitly say the following sentence - \"`Only provide the determined category and nothing else. I don't want any explanations.\"` The reason for this is that chat models tend to be chatty 🥰 and provide additional reasoning and information about why a certain prediction was made. This might be necessary when a human is chatting with the LLM but is not required when we are using the LLM to perform information extraction. More generally, we might want to have the information provided back to us in a structured and rigid form and not as a plain text field.\n",
        "\n",
        "This is exactly where the function calling capability comes in. It is a way to force the OpenAI API to provide the output in a structured manner. The way we achieve this is by informing the API that we would like to use the output of the model in a further call to a separate function. We provide it with a definition of this function and it then structures the output so that it could be easily used as the input to this function.\n",
        "\n",
        "**NOTE**: However, please note that this function does not have to actually exist! It is what we provide in the call to the API and whether we choose to use the output to call this function or not is completely up to us.\n",
        "\n",
        "Let's see how this works in practice."
      ],
      "metadata": {
        "id": "NlyvWOSJtxKp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "review_number= 54\n",
        "review_text = df['text'].iloc[review_number]\n",
        "print (review_text)"
      ],
      "metadata": {
        "id": "Kr7ye4UxwMWK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa09aad0-2cbd-4408-c9d8-b697bf35c2d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Made my hair brittle and dull looking, didn't do anything for the itch or dandruff.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What we do is that we remove the systemPrompt that we had defined in the previous step and instead provide a description of a function called `forward_review_using_sentiment`.\n",
        "\n",
        "- We provide additional information about the functionality of this function - that it is used to forward the customer review to an agent based on whether it is positive or negative.\n",
        "- In order for this function to work, it needs the mandatory field called `sentiment` based on which it will do the forwarding\n",
        "- We also provide additional information on what the sentiment field looks like `type:string` and a `description` of what it means\n",
        "- We also force the model that it needs to call the function by setting the parameter `function_call` to the name of the function. The other attributes are `function_call:none` when you want the model to generate a regular chat message response or `function_call:auto` when you want the model to decide on it's own what it would like to do.\n",
        "\n",
        "Note that we have not actually defined the function and at the moment we do not intend to use it as a forwarding function. But this is a way to force the model to identify the sentiment and provide the response in a structured JSON format."
      ],
      "metadata": {
        "id": "YAjyqcDkxla8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "completion = openai.ChatCompletion.create(\n",
        "    model=\"gpt-3.5-turbo\",\n",
        "    messages=[{\"role\": \"user\", \"content\": review_text}],\n",
        "    functions=[\n",
        "    {\n",
        "        \"name\": \"forward_review_using_sentiment\",\n",
        "        \"description\": \"This function forwards the review to an appropriate customer service agent based on whether it is POSITIVE or NEGATIVE.\",\n",
        "        \"parameters\": {\n",
        "            \"type\": \"object\",\n",
        "            \"properties\": {\n",
        "                \"sentiment\": {\n",
        "                    \"type\": \"string\",\n",
        "                    \"description\": \"The sentiment of the review which is POSITIVE if the customer liked the product and is NEGATIVE if the customer did not like the product\",\n",
        "                },\n",
        "                \"unit\": {\"type\": \"string\"},\n",
        "            },\n",
        "            \"required\": [\"sentiment\"],\n",
        "        },\n",
        "    }\n",
        "    ],\n",
        "    function_call={\"name\": \"forward_review_using_sentiment\"}\n",
        "    )"
      ],
      "metadata": {
        "id": "2--k_IyqNtBy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "completion"
      ],
      "metadata": {
        "id": "wGNm07Zyxd7U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52397730-e390-4912-96aa-9b7f8873161d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<OpenAIObject chat.completion id=chatcmpl-7oDDuL2wJKt2HCSKrVnGGp5Ok07Hq at 0x7a8c7f7054e0> JSON: {\n",
              "  \"id\": \"chatcmpl-7oDDuL2wJKt2HCSKrVnGGp5Ok07Hq\",\n",
              "  \"object\": \"chat.completion\",\n",
              "  \"created\": 1692201586,\n",
              "  \"model\": \"gpt-3.5-turbo-0613\",\n",
              "  \"choices\": [\n",
              "    {\n",
              "      \"index\": 0,\n",
              "      \"message\": {\n",
              "        \"role\": \"assistant\",\n",
              "        \"content\": null,\n",
              "        \"function_call\": {\n",
              "          \"name\": \"forward_review_using_sentiment\",\n",
              "          \"arguments\": \"{\\n  \\\"sentiment\\\": \\\"NEGATIVE\\\"\\n}\"\n",
              "        }\n",
              "      },\n",
              "      \"finish_reason\": \"stop\"\n",
              "    }\n",
              "  ],\n",
              "  \"usage\": {\n",
              "    \"prompt_tokens\": 127,\n",
              "    \"completion_tokens\": 11,\n",
              "    \"total_tokens\": 138\n",
              "  }\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see in the response, the output is in the form of a structured JSON response. The model has determined that the function `forward_review_using_sentiment` must be called with the argument `sentiment` that has the NEGATIVE value. We can easily parse this JSON response and use it for our purposes.\n",
        "\n",
        "You can imagine that there might be scenarios where you want the output in a structured format so that you can actually call another function (e.g. a plugin) with the output. We will actually make use of the function_calling capability in the project associated with the course.\n",
        "\n",
        "Note that this is not the only way to get structured output from the model. You can also explicitly ask it to provide the output in a structured JSON format but this may not work always as expected. In addition, you would also need to provide an example of what this JSON format looks like. The function calling capability was introduced to take care of exactly these kinds of scenarios."
      ],
      "metadata": {
        "id": "vUfZpaavzlkT"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UuVyFSGKsFsz"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}